GAMA-15 master catalogue

Checks and diagnostics

from herschelhelp_internal import git_version
print("This notebook was run with herschelhelp_internal version: \n{}".format(git_version()))

This notebook was run with herschelhelp_internal version: 
04829ed (Thu Nov 2 16:57:19 2017 +0000)

%matplotlib inline
#%config InlineBackend.figure_format = 'svg'

import matplotlib.pyplot as plt
plt.rc('figure', figsize=(10, 6))
plt.style.use('ggplot')

import locale
locale.setlocale(locale.LC_ALL, 'en_GB')

import os
import time
import itertools

from astropy.coordinates import SkyCoord
from astropy.table import Table
from astropy import units as u
from astropy import visualization as vis
import numpy as np
from matplotlib_venn import venn3, venn2

from herschelhelp_internal.masterlist import nb_compare_mags, nb_ccplots, nb_histograms, find_last_ml_suffix

OUT_DIR = os.environ.get('OUT_DIR', "./data")
SUFFIX = find_last_ml_suffix()
#SUFFIX = "20171016"

master_catalogue_filename = "master_catalogue_gama-15_{}.fits".format(SUFFIX)
master_catalogue = Table.read("{}/{}".format(OUT_DIR, master_catalogue_filename))

print("Diagnostics done using: {}".format(master_catalogue_filename))

Diagnostics done using: master_catalogue_gama-15_20171120.fits

I - Summary of wavelength domains

flag_obs = master_catalogue['flag_optnir_obs']
flag_det = master_catalogue['flag_optnir_det']

venn2(
    [
        np.sum(flag_obs == 1),
        np.sum(flag_obs == 2),
        np.sum(flag_obs == 3)
    ],
    set_labels=('Optical', 'near-IR'),
    subset_label_formatter=lambda x: "{}%".format(int(100*x/len(flag_obs)))
)
plt.title("Wavelength domain observations");

venn2(
    [
        np.sum(flag_det[flag_obs == 3] == 1),
        np.sum(flag_det[flag_obs == 3] == 2),
        np.sum(flag_det[flag_obs == 3] == 3)
    ],
    set_labels=('near-IR', 'Optical'),
    subset_label_formatter=lambda x: "{}%".format(int(100*x/np.sum(flag_det != 0)))
)
plt.title("Detection of the {} sources detected\n in any wavelength domains "
          "(among {} sources)".format(
              locale.format('%d', np.sum(flag_det != 0), grouping=True),
              locale.format('%d', len(flag_det), grouping=True)));

II - Comparing magnitudes in similar filters

The master list if composed of several catalogues containing magnitudes in similar filters on different instruments. We are comparing the magnitudes in these corresponding filters.

u_bands = [           "KIDS u"]
g_bands = ["DECam g", "KIDS g", "SUPRIME g", "GPC1 g"]
r_bands = ["DECam r", "KIDS r", "SUPRIME r", "GPC1 r"]
i_bands = ["DECam i", "KIDS i", "SUPRIME i", "GPC1 i"]
z_bands = ["DECam z",           "SUPRIME z", "GPC1 z"]
y_bands = ["DECam y",           "SUPRIME y", "GPC1 y"]

II.a - Comparing depths

We compare the histograms of the total aperture magnitudes of similar bands.

for bands in [u_bands, g_bands, r_bands, i_bands, z_bands, y_bands]:
    colnames = ["m_{}".format(band.replace(" ", "_").lower()) for band in bands]
    nb_histograms(master_catalogue, colnames, bands)

HELP warning: the column m_decam_g (DECam g) is empty.

HELP warning: the column m_decam_z (DECam z) is empty.

II.b - Comparing magnitudes

We compare one to one each magnitude in similar bands.

for band_of_a_kind in [u_bands, g_bands, r_bands, i_bands, z_bands, y_bands]:
    for band1, band2 in itertools.combinations(band_of_a_kind, 2):
        
        basecol1, basecol2 = band1.replace(" ", "_").lower(), band2.replace(" ", "_").lower()
        
        col1, col2 = "m_ap_{}".format(basecol1), "m_ap_{}".format(basecol2)
        nb_compare_mags(master_catalogue[col1], master_catalogue[col2], 
                        labels=("{} (aperture)".format(band1), "{} (aperture)".format(band2)))
        
        col1, col2 = "m_{}".format(basecol1), "m_{}".format(basecol2)
        nb_compare_mags(master_catalogue[col1], master_catalogue[col2], 
                        labels=("{} (total)".format(band1), "{} (total)".format(band2)))

No sources have both DECam g (aperture) and KIDS g (aperture) values.
No sources have both DECam g (total) and KIDS g (total) values.
No sources have both DECam g (aperture) and SUPRIME g (aperture) values.
No sources have both DECam g (total) and SUPRIME g (total) values.
No sources have both DECam g (aperture) and GPC1 g (aperture) values.
No sources have both DECam g (total) and GPC1 g (total) values.
SUPRIME g (aperture) - KIDS g (aperture):
- Median: -0.10
- Median Absolute Deviation: 0.15
- 1% percentile: -1.639749526977539
- 99% percentile: 0.9492244338989275

SUPRIME g (total) - KIDS g (total):
- Median: -0.28
- Median Absolute Deviation: 0.25
- 1% percentile: -2.5640608215332032
- 99% percentile: 1.1282776641845618

GPC1 g (aperture) - KIDS g (aperture):
- Median: -0.31
- Median Absolute Deviation: 0.38
- 1% percentile: -3.1371240997314453
- 99% percentile: 2.213498001098639

GPC1 g (total) - KIDS g (total):
- Median: -0.12
- Median Absolute Deviation: 0.25
- 1% percentile: -3.543268280029297
- 99% percentile: 1.5813421630859281

GPC1 g (aperture) - SUPRIME g (aperture):
- Median: -0.28
- Median Absolute Deviation: 0.43
- 1% percentile: -3.1680870056152344
- 99% percentile: 2.4179883003234885

GPC1 g (total) - SUPRIME g (total):
- Median: -0.04
- Median Absolute Deviation: 0.30
- 1% percentile: -3.2857778167724607
- 99% percentile: 1.7586553955078141

KIDS r (aperture) - DECam r (aperture):
- Median: -0.70
- Median Absolute Deviation: 0.44
- 1% percentile: -3.8088677978515624
- 99% percentile: 0.6727523803710938

KIDS r (total) - DECam r (total):
- Median: -0.78
- Median Absolute Deviation: 0.45
- 1% percentile: -3.6640729904174805
- 99% percentile: 0.9124132156372005

SUPRIME r (aperture) - DECam r (aperture):
- Median: -0.68
- Median Absolute Deviation: 0.45
- 1% percentile: -3.9173989295959473
- 99% percentile: 1.1831946372985864

SUPRIME r (total) - DECam r (total):
- Median: -0.93
- Median Absolute Deviation: 0.43
- 1% percentile: -4.053550262451172
- 99% percentile: 1.1088246154785022

GPC1 r (aperture) - DECam r (aperture):
- Median: -1.09
- Median Absolute Deviation: 0.39
- 1% percentile: -3.7203458976745605
- 99% percentile: 0.5200489234924243

GPC1 r (total) - DECam r (total):
- Median: -0.93
- Median Absolute Deviation: 0.40
- 1% percentile: -3.610483169555664
- 99% percentile: 0.6347942352294932

SUPRIME r (aperture) - KIDS r (aperture):
- Median: -0.06
- Median Absolute Deviation: 0.11
- 1% percentile: -0.852659034729004
- 99% percentile: 0.8960716247558587

SUPRIME r (total) - KIDS r (total):
- Median: -0.19
- Median Absolute Deviation: 0.18
- 1% percentile: -1.4241687774658205
- 99% percentile: 1.1404188156127932

GPC1 r (aperture) - KIDS r (aperture):
- Median: -0.11
- Median Absolute Deviation: 0.21
- 1% percentile: -1.351493549346924
- 99% percentile: 1.496449184417728

GPC1 r (total) - KIDS r (total):
- Median: 0.05
- Median Absolute Deviation: 0.13
- 1% percentile: -1.5403207397460938
- 99% percentile: 1.16571809768676

GPC1 r (aperture) - SUPRIME r (aperture):
- Median: -0.10
- Median Absolute Deviation: 0.23
- 1% percentile: -1.5872383117675781
- 99% percentile: 1.525237350463859

GPC1 r (total) - SUPRIME r (total):
- Median: 0.11
- Median Absolute Deviation: 0.15
- 1% percentile: -1.6637313461303709
- 99% percentile: 1.2613188362121595

KIDS i (aperture) - DECam i (aperture):
- Median: -0.37
- Median Absolute Deviation: 0.33
- 1% percentile: -2.8999361419677734
- 99% percentile: 1.0785923767089827

KIDS i (total) - DECam i (total):
- Median: -0.32
- Median Absolute Deviation: 0.32
- 1% percentile: -1.9150989532470704
- 99% percentile: 1.9198204040527456

SUPRIME i (aperture) - DECam i (aperture):
- Median: -0.48
- Median Absolute Deviation: 0.35
- 1% percentile: -3.4942554473876957
- 99% percentile: 0.8745538139343247

SUPRIME i (total) - DECam i (total):
- Median: -0.59
- Median Absolute Deviation: 0.33
- 1% percentile: -2.955655212402344
- 99% percentile: 1.211650524139405

GPC1 i (aperture) - DECam i (aperture):
- Median: -0.50
- Median Absolute Deviation: 0.27
- 1% percentile: -2.4328741073608398
- 99% percentile: 0.43892814636230604

GPC1 i (total) - DECam i (total):
- Median: -0.30
- Median Absolute Deviation: 0.27
- 1% percentile: -1.7496144104003906
- 99% percentile: 0.6908638000488327

SUPRIME i (aperture) - KIDS i (aperture):
- Median: -0.07
- Median Absolute Deviation: 0.18
- 1% percentile: -2.0469532012939453
- 99% percentile: 1.169138336181641

SUPRIME i (total) - KIDS i (total):
- Median: -0.27
- Median Absolute Deviation: 0.27
- 1% percentile: -3.016033172607422
- 99% percentile: 1.297071933746338

GPC1 i (aperture) - KIDS i (aperture):
- Median: -0.08
- Median Absolute Deviation: 0.16
- 1% percentile: -0.9477005386352539
- 99% percentile: 0.818601818084717

GPC1 i (total) - KIDS i (total):
- Median: 0.05
- Median Absolute Deviation: 0.10
- 1% percentile: -0.8343010139465332
- 99% percentile: 0.6969108963012687

GPC1 i (aperture) - SUPRIME i (aperture):
- Median: -0.03
- Median Absolute Deviation: 0.18
- 1% percentile: -1.1367273139953613
- 99% percentile: 0.9213391876220709

GPC1 i (total) - SUPRIME i (total):
- Median: 0.20
- Median Absolute Deviation: 0.11
- 1% percentile: -0.8914554595947265
- 99% percentile: 0.7686335754394538

No sources have both DECam z (aperture) and SUPRIME z (aperture) values.
No sources have both DECam z (total) and SUPRIME z (total) values.
No sources have both DECam z (aperture) and GPC1 z (aperture) values.
No sources have both DECam z (total) and GPC1 z (total) values.
GPC1 z (aperture) - SUPRIME z (aperture):
- Median: -0.04
- Median Absolute Deviation: 0.20
- 1% percentile: -1.1917795181274413
- 99% percentile: 1.2728551101684569

GPC1 z (total) - SUPRIME z (total):
- Median: 0.18
- Median Absolute Deviation: 0.12
- 1% percentile: -1.0532553100585937
- 99% percentile: 0.9818671417236327

SUPRIME y (aperture) - DECam y (aperture):
- Median: -0.08
- Median Absolute Deviation: 0.22
- 1% percentile: -2.479114055633545
- 99% percentile: 1.2441625595092773

SUPRIME y (total) - DECam y (total):
- Median: -0.09
- Median Absolute Deviation: 0.15
- 1% percentile: -1.1775811195373536
- 99% percentile: 1.5071928024292074

GPC1 y (aperture) - DECam y (aperture):
- Median: -0.12
- Median Absolute Deviation: 0.18
- 1% percentile: -2.1188339138031007
- 99% percentile: 1.5731890678405795

GPC1 y (total) - DECam y (total):
- Median: 0.08
- Median Absolute Deviation: 0.20
- 1% percentile: -1.8769407653808594
- 99% percentile: 1.6991668701171863

GPC1 y (aperture) - SUPRIME y (aperture):
- Median: -0.19
- Median Absolute Deviation: 0.28
- 1% percentile: -1.8443033599853516
- 99% percentile: 1.679386215209945

GPC1 y (total) - SUPRIME y (total):
- Median: 0.09
- Median Absolute Deviation: 0.23
- 1% percentile: -2.0898357391357423
- 99% percentile: 1.6052894592285112

III - Comparing magnitudes to reference bands

Cross-match the master list to SDSS and 2MASS to compare its magnitudes to SDSS and 2MASS ones.

master_catalogue_coords = SkyCoord(master_catalogue['ra'], master_catalogue['dec'])

III.a - Comparing u, g, r, i, and z bands to SDSS

The catalogue is cross-matched to SDSS-DR13 withing 0.2 arcsecond.

We compare the u, g, r, i, and z magnitudes to those from SDSS using fiberMag for the aperture magnitude and petroMag for the total magnitude.

sdss = Table.read("../../dmu0/dmu0_SDSS-DR13/data/SDSS-DR13_GAMA-15.fits")
sdss_coords = SkyCoord(sdss['ra'] * u.deg, sdss['dec'] * u.deg)

idx, d2d, _ = sdss_coords.match_to_catalog_sky(master_catalogue_coords)
mask = (d2d < 0.2 * u.arcsec)

sdss = sdss[mask]
ml_sdss_idx = idx[mask]

for band_of_a_kind in [u_bands, g_bands, r_bands, i_bands, z_bands]:
    for band in band_of_a_kind:
        
        sdss_mag_ap_colname = "fiberMag_{}".format(band[-1])
        sdss_mag_tot_colname = "petroMag_{}".format(band[-1])
        master_cat_mag_ap_colname = "m_ap_{}".format(band.replace(" ", "_").lower())
        master_cat_mag_tot_colname = "m_ap_{}".format(band.replace(" ", "_").lower())
        
        # Set SDSS magnitudes to NaN when the magnitude  is negative as SDSS uses large
        # negative numbers for missing magnitudes
        sdss[sdss_mag_ap_colname][sdss[sdss_mag_ap_colname] < 0.] = np.nan
        sdss[sdss_mag_tot_colname][sdss[sdss_mag_tot_colname] < 0.] = np.nan
        
        sdss_mag_ap = sdss[sdss_mag_ap_colname]
        master_cat_mag_ap = master_catalogue[master_cat_mag_ap_colname][ml_sdss_idx]
    
        nb_compare_mags(sdss_mag_ap, master_cat_mag_ap,
                        labels=("SDSS {} (fiberMag)".format(band[-1]), "{} (aperture)".format(band)))
    
        sdss_mag_tot = sdss[sdss_mag_tot_colname]
        master_cat_mag_tot = master_catalogue[master_cat_mag_tot_colname][ml_sdss_idx]
        
        nb_compare_mags(sdss_mag_ap, master_cat_mag_ap,
                        labels=("SDSS {} (petroMag)".format(band[-1]), "{} (total)".format(band)))

KIDS u (aperture) - SDSS u (fiberMag):
- Median: 0.06
- Median Absolute Deviation: 0.42
- 1% percentile: -1.6777833557128907
- 99% percentile: 2.9605518531799304

KIDS u (total) - SDSS u (petroMag):
- Median: 0.06
- Median Absolute Deviation: 0.42
- 1% percentile: -1.6777833557128907
- 99% percentile: 2.9605518531799304

No sources have both SDSS g (fiberMag) and DECam g (aperture) values.
No sources have both SDSS g (petroMag) and DECam g (total) values.
KIDS g (aperture) - SDSS g (fiberMag):
- Median: -0.21
- Median Absolute Deviation: 0.15
- 1% percentile: -1.0146896362304687
- 99% percentile: 0.964009094238282

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/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:12: RuntimeWarning: invalid value encountered in less

KIDS g (total) - SDSS g (petroMag):
- Median: -0.21
- Median Absolute Deviation: 0.15
- 1% percentile: -1.0146896362304687
- 99% percentile: 0.964009094238282

SUPRIME g (aperture) - SDSS g (fiberMag):
- Median: -0.29
- Median Absolute Deviation: 0.16
- 1% percentile: -1.101328353881836
- 99% percentile: 0.9030104446411131

SUPRIME g (total) - SDSS g (petroMag):
- Median: -0.29
- Median Absolute Deviation: 0.16
- 1% percentile: -1.101328353881836
- 99% percentile: 0.9030104446411131

GPC1 g (aperture) - SDSS g (fiberMag):
- Median: -0.50
- Median Absolute Deviation: 0.29
- 1% percentile: -2.722372722625732
- 99% percentile: 1.9057563209533683

GPC1 g (total) - SDSS g (petroMag):
- Median: -0.50
- Median Absolute Deviation: 0.29
- 1% percentile: -2.722372722625732
- 99% percentile: 1.9057563209533683

DECam r (aperture) - SDSS r (fiberMag):
- Median: 0.59
- Median Absolute Deviation: 0.37
- 1% percentile: -0.4526587295532226
- 99% percentile: 2.8505001258849787

DECam r (total) - SDSS r (petroMag):
- Median: 0.59
- Median Absolute Deviation: 0.37
- 1% percentile: -0.4526587295532226
- 99% percentile: 2.8505001258849787

KIDS r (aperture) - SDSS r (fiberMag):
- Median: -0.26
- Median Absolute Deviation: 0.11
- 1% percentile: -0.9293210601806641
- 99% percentile: 0.3801486206054674

KIDS r (total) - SDSS r (petroMag):
- Median: -0.26
- Median Absolute Deviation: 0.11
- 1% percentile: -0.9293210601806641
- 99% percentile: 0.3801486206054674

SUPRIME r (aperture) - SDSS r (fiberMag):
- Median: -0.31
- Median Absolute Deviation: 0.13
- 1% percentile: -0.9976795959472656
- 99% percentile: 0.5201865768432602

SUPRIME r (total) - SDSS r (petroMag):
- Median: -0.31
- Median Absolute Deviation: 0.13
- 1% percentile: -0.9976795959472656
- 99% percentile: 0.5201865768432602

GPC1 r (aperture) - SDSS r (fiberMag):
- Median: -0.38
- Median Absolute Deviation: 0.15
- 1% percentile: -1.4835214614868164
- 99% percentile: 0.963675765991213

GPC1 r (total) - SDSS r (petroMag):
- Median: -0.38
- Median Absolute Deviation: 0.15
- 1% percentile: -1.4835214614868164
- 99% percentile: 0.963675765991213

DECam i (aperture) - SDSS i (fiberMag):
- Median: 0.04
- Median Absolute Deviation: 0.22
- 1% percentile: -0.7525212097167968
- 99% percentile: 1.6925888824462914

DECam i (total) - SDSS i (petroMag):
- Median: 0.04
- Median Absolute Deviation: 0.22
- 1% percentile: -0.7525212097167968
- 99% percentile: 1.6925888824462914

KIDS i (aperture) - SDSS i (fiberMag):
- Median: -0.26
- Median Absolute Deviation: 0.11
- 1% percentile: -0.9334051704406738
- 99% percentile: 0.4504371833801264

KIDS i (total) - SDSS i (petroMag):
- Median: -0.26
- Median Absolute Deviation: 0.11
- 1% percentile: -0.9334051704406738
- 99% percentile: 0.4504371833801264

SUPRIME i (aperture) - SDSS i (fiberMag):
- Median: -0.32
- Median Absolute Deviation: 0.12
- 1% percentile: -1.0101194763183594
- 99% percentile: 0.5216849517822294

SUPRIME i (total) - SDSS i (petroMag):
- Median: -0.32
- Median Absolute Deviation: 0.12
- 1% percentile: -1.0101194763183594
- 99% percentile: 0.5216849517822294

GPC1 i (aperture) - SDSS i (fiberMag):
- Median: -0.36
- Median Absolute Deviation: 0.12
- 1% percentile: -1.0264884567260741
- 99% percentile: 0.4694122505188034

GPC1 i (total) - SDSS i (petroMag):
- Median: -0.36
- Median Absolute Deviation: 0.12
- 1% percentile: -1.0264884567260741
- 99% percentile: 0.4694122505188034

No sources have both SDSS z (fiberMag) and DECam z (aperture) values.
No sources have both SDSS z (petroMag) and DECam z (total) values.
SUPRIME z (aperture) - SDSS z (fiberMag):
- Median: -0.21
- Median Absolute Deviation: 0.19
- 1% percentile: -1.2618440628051757
- 99% percentile: 0.9837815856933585

SUPRIME z (total) - SDSS z (petroMag):
- Median: -0.21
- Median Absolute Deviation: 0.19
- 1% percentile: -1.2618440628051757
- 99% percentile: 0.9837815856933585

GPC1 z (aperture) - SDSS z (fiberMag):
- Median: -0.30
- Median Absolute Deviation: 0.18
- 1% percentile: -1.4637153625488282
- 99% percentile: 1.1003234863281253

GPC1 z (total) - SDSS z (petroMag):
- Median: -0.30
- Median Absolute Deviation: 0.18
- 1% percentile: -1.4637153625488282
- 99% percentile: 1.1003234863281253

III.b - Comparing J and K bands to 2MASS

The catalogue is cross-matched to 2MASS-PSC withing 0.2 arcsecond. We compare the UKIDSS total J and K magnitudes to those from 2MASS.

The 2MASS magnitudes are “Vega-like” and we have to convert them to AB magnitudes using the zero points provided on this page:

Band	Fν - 0 mag (Jy)
J	1594
H	1024
Ks	666.7

In addition, UKIDSS uses a K band whereas 2MASS uses a Ks (“short”) band, this page give a correction to convert the K band in a Ks band with the formula:

$$K_{s(2MASS)} = K_{UKIRT} + 0.003 + 0.004 * (J−K)_{UKIRT}$$

# The AB zero point is 3631 Jy
j_2mass_to_ab = 2.5 * np.log10(3631/1595)
k_2mass_to_ab = 2.5 * np.log10(3631/666.7)

twomass = Table.read("../../dmu0/dmu0_2MASS-point-sources/data/2MASS-PSC_GAMA-15.fits")
twomass_coords = SkyCoord(twomass['raj2000'], twomass['dej2000'])

idx, d2d, _ = twomass_coords.match_to_catalog_sky(master_catalogue_coords)
mask = (d2d < 0.2 * u.arcsec)

twomass = twomass[mask]
ml_twomass_idx = idx[mask]

nb_compare_mags(twomass['jmag'] + j_2mass_to_ab, master_catalogue['m_ukidss_j'][ml_twomass_idx],
                labels=("2MASS J", "UKIDSS J (total)"))

UKIDSS J (total) - 2MASS J:
- Median: 0.06
- Median Absolute Deviation: 0.06
- 1% percentile: -0.7053935164548701
- 99% percentile: 0.6007433978365114

ukidss_ks_like = master_catalogue['m_ukidss_k'] + 0.003 + 0.004 * (
    master_catalogue['m_ukidss_j'] - master_catalogue['m_ukidss_k'])
nb_compare_mags(twomass['kmag'] + k_2mass_to_ab, ukidss_ks_like[ml_twomass_idx],
                labels=("2MASS Ks", "UKIDSS Ks-like (total)"))

UKIDSS Ks-like (total) - 2MASS Ks:
- Median: 0.13
- Median Absolute Deviation: 0.09
- 1% percentile: -0.805631813264706
- 99% percentile: 1.106451120695254

nb_compare_mags(twomass['jmag'] + j_2mass_to_ab, master_catalogue['m_vircam_j'][ml_twomass_idx],
                labels=("2MASS J", "VISTA J (total)"))

VISTA J (total) - 2MASS J:
- Median: 0.04
- Median Absolute Deviation: 0.07
- 1% percentile: -1.0083736126615364
- 99% percentile: 1.1275078088472599

ukidss_ks_like = master_catalogue['m_vircam_k'] + 0.003 + 0.004 * (
    master_catalogue['m_ukidss_j'] - master_catalogue['m_ukidss_k'])
nb_compare_mags(twomass['kmag'] + k_2mass_to_ab, ukidss_ks_like[ml_twomass_idx],
                labels=("2MASS Ks", "VIRCAM Ks (total)"))

VIRCAM Ks (total) - 2MASS Ks:
- Median: 0.06
- Median Absolute Deviation: 0.11
- 1% percentile: -0.9973885948809169
- 99% percentile: 1.2021790728894632

IV - Comparing aperture magnitudes to total ones.

nb_ccplots(
    master_catalogue['m_suprime_r'],
    master_catalogue['m_ap_suprime_r'] - master_catalogue['m_suprime_r'],
    "r total magnitude (HSC)", "r aperture mag - total mag (HSC)",
    master_catalogue["stellarity"],
    invert_x=True
)

Number of source used: 5937538 / 14232884 (41.72%)

V - Color-color and magnitude-color plots

nb_ccplots(
    master_catalogue['m_suprime_g'] - master_catalogue['m_suprime_i'],
    master_catalogue['m_ukidss_j'] - master_catalogue['m_ukidss_k'],
    "g - i (HSC)", "J - K (UKIDSS)",
    master_catalogue["stellarity"]
)

/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:2: RuntimeWarning: invalid value encountered in subtract
  from ipykernel import kernelapp as app

Number of source used: 68178 / 14232884 (0.48%)

nb_ccplots(
    master_catalogue['m_suprime_i'] - master_catalogue['m_vircam_j'],
    master_catalogue['m_suprime_g'] - master_catalogue['m_vircam_k'],
    "WFC i - IRAC1", "g - i (WFC)",
    master_catalogue["stellarity"]
)

Number of source used: 279701 / 14232884 (1.97%)

nb_ccplots(
    master_catalogue['m_ukidss_j'] - master_catalogue['m_ukidss_k'],
    master_catalogue['m_suprime_g'] - master_catalogue['m_ukidss_j'],
    "J - K (UKIDSS)", "g - J (HSC, UKIDSS)",
    master_catalogue["stellarity"]
)

Number of source used: 68180 / 14232884 (0.48%)

nb_ccplots(
    master_catalogue['m_suprime_i'] - master_catalogue['m_suprime_z'],
    master_catalogue['m_suprime_z'] - master_catalogue['m_ukidss_j'],
    "i - z (HSC)", "z - J (HSC, UKIDSS)",
    master_catalogue["stellarity"]
)

/opt/anaconda3/envs/herschelhelp_internal/lib/python3.6/site-packages/ipykernel/__main__.py:2: RuntimeWarning: invalid value encountered in subtract
  from ipykernel import kernelapp as app

Number of source used: 84746 / 14232884 (0.60%)